AI Agent Memory Systems: Short-Term and Long-Term Memory

Build AI agents that remember and learn from interactions

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AI Agent Memory Systems: Short-Term and Long-Term Memory

Build AI agents that remember and learn from interactions

Design and implement memory systems for AI agents including working memory, episodic memory, and semantic memory. Learn how to give agents persistent context and personalization.

AI Agent Memory Systems

Why Memory Matters for AI Agents

Without memory, AI agents are stateless - every conversation starts fresh. Memory enables:
  • Personalization based on past interactions
  • Learning from mistakes
  • Maintaining context across sessions
  • Building relationships over time
  • Types of Memory

    1. Working Memory (In-Context)

    Short-term memory stored directly in the conversation context:
    python
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "My name is Alice"},
        {"role": "assistant", "content": "Nice to meet you, Alice!"},
        {"role": "user", "content": "What's my name?"},  # Agent can answer from context
    ]
    

    2. Episodic Memory

    Records of past interactions stored in a database:
    python
    class EpisodicMemory:
        def __init__(self, db):
            self.db = db
        
        def store(self, user_id, interaction):
            self.db.insert({
                "user_id": user_id,
                "timestamp": datetime.now(),
                "summary": interaction.summary,
                "key_facts": interaction.extract_facts()
            })
        
        def retrieve(self, user_id, query, k=5):
            return self.db.semantic_search(
                query=query,
                filter={"user_id": user_id},
                limit=k
            )
    

    3. Semantic Memory (Knowledge Base)

    Factual knowledge stored as embeddings:
    python
    from langchain.vectorstores import Chroma
    from langchain.embeddings import OpenAIEmbeddings

    semantic_memory = Chroma( embedding_function=OpenAIEmbeddings() )

    Store facts

    semantic_memory.add_texts([ "Alice prefers Python over JavaScript", "Alice works at TechCorp as a data scientist", "Alice is interested in machine learning" ])

    Retrieve relevant context

    results = semantic_memory.similarity_search( "What programming language does the user prefer?" )

    Memory Management

    As memories accumulate, you need strategies to:
  • Summarize old conversations
  • Prioritize important memories
  • Forget irrelevant information
  • Handle contradictions
  • Privacy Considerations

    Always implement memory with user consent and provide options to view/delete stored memories.

    相关工具

    langchainchromapineconeopenai